Annotation waveform

ABSTRACT

Systems and methods for processing cardiac information include a processing unit configured to receive a set of cardiac electrical signals; receive an indication of a measurement location corresponding to each of the set of electrical signals; and identify, for each electrical signal of the set of electrical signals, a deflection. The deflection includes a deviation from a signal baseline. An activation waveform corresponding to the set of electrical signals is generated based on the identified deflections.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/955,450, filed Apr. 17, 2018, which claims priority to ProvisionalApplication No. 62/486,926, filed Apr. 18, 2017, both of which areherein incorporated by reference in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein relates to medical systems andmethods for mapping an anatomical space of the body. More specifically,the disclosure relates to systems and methods for cardiac mapping.

BACKGROUND

Use of minimally invasive procedures, such as catheter ablation, totreat a variety of heart conditions, such as supraventricular andventricular arrhythmias, is becoming increasingly more prevalent. Suchprocedures involve the mapping of electrical activity in the heart(e.g., based on cardiac signals), such as at various locations on theendocardium surface (“cardiac mapping”), to identify the site of originof the arrhythmia followed by a targeted ablation of the site. Toperform such cardiac mapping a catheter with one or more electrodes canbe inserted into the patient's heart chamber.

Conventional three-dimensional (3D) mapping techniques include contactmapping and non-contact mapping, and may employ a combination of contactand non-contact mapping. In both techniques, one or more catheters areadvanced into the heart. With some catheters, once in the chamber, thecatheter may be deployed to assume a 3D shape. In contact mapping,physiological signals resulting from the electrical activity of theheart are acquired with one or more electrodes located at the catheterdistal tip after determining that the tip is in stable and steadycontact with the endocardium surface of a particular heart chamber. Innon-contact-based mapping systems, using the signals detected by thenon-contact electrodes and information on chamber anatomy and relativeelectrode location, the system provides physiological informationregarding the endocardium of the heart chamber. Location and electricalactivity is usually measured sequentially on a point-by-point basis atabout 50 to 200 points on the internal surface of the heart to constructan electro-anatomical depiction of the heart. The generated map may thenserve as the basis for deciding on a therapeutic course of action, forexample, tissue ablation, to alter the propagation of the heart'selectrical activity and to restore normal heart rhythm.

In many conventional mapping systems, the clinician visually inspects orexamines the captured electrograms (EGMs), which increases examinationtime and cost. During an automatic electro-anatomical mapping process,however, approximately 6,000 to 20,000 intracardiac electrograms (EGMs)may be captured, which does not lend itself to being manually inspectedin full by a clinician (e.g., a physician) for a diagnostic assessment,EGM categorization, and/or the like. Typically mapping systems extractscalar values from each EGM to construct voltage, activation, or othermap types to depict overall patterns of activity within the heart. Whilemaps reduce the need to inspect the captured EGMs, they also condensethe often complex and useful information in the EGMs. Furthermore, mapsmay be misleading due to electrical artifacts or inappropriate selectionof features such as activation times. Additionally, due to the complexnature of conventional techniques, cardiac maps often are not suitablefor accurate and efficient interpretation.

SUMMARY

In an Example 1, a system for processing cardiac information, the systemcomprising: a processing unit configured to: receive a set of cardiacelectrical signals; receive an indication of a measurement locationcorresponding to each of the set of electrical signals; identify, foreach electrical signal of the set of electrical signals, a deflection,the deflection comprising a deviation from a signal baseline; andgenerate, based on the identified deflections, an activation waveformcorresponding to the set of electrical signals.

In an Example 2, the system of Example 1, wherein the processing unit isfurther configured to generate one or more cardiac map annotations basedon the activation waveform.

In an Example 3, the system of Example 2, wherein the processing unit isfurther configured to facilitate presentation, on a display device, ofthe cardiac map.

In an Example 4, the system of any of Examples 1-3, wherein theprocessing unit is further configured to determine the signal baseline.

In an Example 5, the system of Example 4, wherein the processing unit isconfigured to determine the signal baseline by referencing a memory inwhich the signal baseline is stored, the signal baseline comprising apre-determined value.

In an Example 6, the system of Example 4, wherein the processing unit isconfigured to determine the signal baseline based on an estimated noisefloor associated with at least one electrical signal of the set ofelectrical signals.

In an Example 7, the system of any of Examples 1-6, wherein theprocessing unit is configured to identify, for each electrical signal ofthe set of electrical signals, a deflection by identifying, for eachsample point of each electrical signal, whether the sample pointrepresents a deviation from the signal baseline.

In an Example 8, the system of any of Examples 1-7, wherein theprocessing unit is configured to identify, for each electrical signal ofthe set of electrical signals, a deflection by determining, for eachsample point of each electrical signal, an activation waveform value.

In an Example 9, the system of Example 8, wherein the activationwaveform value comprises a probability that the identified deflectioncorresponding to the sample point represents an activation.

In an Example 10, the system of any of Examples 1-9, the set ofelectrical signals comprising a plurality of electrical signals, whereinthe processing unit is configured to adjust an activation waveform valuecorresponding to a sample point based on an evaluation of spatiotemporalconsistency of corresponding deflections of the plurality of electricalsignals.

In an Example 11, the system of any of Examples 1-10, wherein theprocessing unit is further configured to identify far-field signalcomponents of each of the set of electrical signals.

In an Example 12, the system of any of Examples 1-11, wherein theprocessing unit is further configured to suppress far-field signalcomponents of each of the set of electrical signals.

In an Example 13, a method of processing cardiac information,comprising: receiving a set of cardiac electrical signals; receiving anindication of a measurement location corresponding to each of the set ofelectrical signals; identifying, for each electrical signal of the setof electrical signals, a deflection, the deflection comprising adeviation from a signal baseline; and generating, based on theidentified deflections, an activation waveform corresponding to the setof electrical signals.

In an Example 14, the method of Example 13, further comprisinggenerating one or more cardiac map annotations based on the activationwaveform.

In an Example 15, the method of either of Examples 13 or 14, the set ofelectrical signals comprising a plurality of electrical signals, themethod further comprising adjusting an activation waveform valuecorresponding to a sample point based on an evaluation of spatiotemporalconsistency of corresponding deflections of the plurality of electricalsignals.

In an Example 16, a system for processing cardiac information, thesystem comprising: a processing unit configured to: receive a set ofcardiac electrical signals; receive an indication of a measurementlocation corresponding to each of the set of electrical signals;identify, for each electrical signal of the set of electrical signals, adeflection, the deflection comprising a deviation from a signalbaseline; generate, based on the identified deflections, an activationwaveform corresponding to the set of electrical signals; and facilitatepresentation, on a display device and based on the activation waveform,of at least one of a cardiac map, a representation of the activationwaveform, and a representation of an activation histogram.

In an Example 17, the system of Example 16, wherein the processing unitis further configured to generate one or more cardiac map annotationsbased on the activation waveform.

In an Example 18, the system of Example 16, wherein the processing unitis further configured to determine the signal baseline.

In an Example 19, the system of Example 18, wherein the processing unitis configured to determine the signal baseline by referencing a memoryin which the signal baseline is stored, the signal baseline comprising apre-determined value.

In an Example 20, the system of Example 18, wherein the processing unitis configured to determine the signal baseline based on an estimatednoise floor associated with at least one electrical signal of the set ofelectrical signals.

In an Example 21, the system of Example 16, wherein the processing unitis configured to identify, for each electrical signal of the set ofelectrical signals, a deflection by identifying, for each sample pointof each electrical signal, whether the sample point represents adeviation from the signal baseline.

In an Example 22, the system of Example 16, wherein the processing unitis configured to identify, for each electrical signal of the set ofelectrical signals, a deflection by determining, for each sample pointof each electrical signal, an activation waveform value.

In an Example 23, the system of Example 22, wherein the activationwaveform value comprises a probability that the identified deflectioncorresponding to the sample point represents an activation.

In an Example 24, the system of Example 16, the set of electricalsignals comprising a plurality of electrical signals, wherein theprocessing unit is configured to adjust an activation waveform valuecorresponding to a sample point based on an evaluation of spatiotemporalconsistency of corresponding deflections of the plurality of electricalsignals.

In an Example 25, the system of Example 16, wherein the processing unitis further configured to identify far-field signal components of each ofthe set of electrical signals.

In an Example 26, the system of Example 16, wherein the processing unitis further configured to suppress far-field signal components of each ofthe set of electrical signals.

In an Example 27, the system of Example 16, further comprising acatheter, wherein the processing unit is configured to receive the setof electrical signals from the catheter, the catheter comprising atleast one of a mapping catheter, a diagnostic catheter, a CS catheter,and an ablation catheter.

In an Example 28, a method of processing cardiac information,comprising: receiving a set of cardiac electrical signals; receiving anindication of a measurement location corresponding to each of the set ofelectrical signals; identifying, for each electrical signal of the setof electrical signals, a deflection, the deflection comprising adeviation from a signal baseline; generating, based on the identifieddeflections, an activation waveform corresponding to the set ofelectrical signals; and facilitating presentation, on a display deviceand based on the activation waveform, of at least one of a cardiac map,a representation of the activation waveform, and a representation of anactivation histogram.

In an Example 29, the method of Example 28, further comprisinggenerating one or more cardiac map annotations based on the activationwaveform.

In an Example 30, the method of Example 28, further comprisingdetermining the signal baseline.

In an Example 31, the method of Example 30, wherein the signal baselineis determined based on an estimated noise floor associated with at leastone electrical signal of the set of electrical signals.

In an Example 32, the method of Example 28, further comprisingidentifying, for each electrical signal of the set of electricalsignals, a deflection by determining, for each sample point of eachelectrical signal, an activation waveform value, wherein the activationwaveform value comprises a probability that the identified deflectioncorresponding to the sample point represents an activation.

In an Example 33, the method of Example 32, the set of electricalsignals comprising a plurality of electrical signals, the method furthercomprising adjusting an activation waveform value corresponding to asample point based on an evaluation of spatiotemporal consistency ofcorresponding deflections of the plurality of electrical signals.

In an Example 34, the method of Example 33, further comprisingidentifying and/or suppressing far-field signal components of each ofthe set of electrical signals.

In an Example 35, one or more computer-readable media having embodiedthereon computer-executable instructions that, when executed by aprocessor, are configured to cause the processor to instantiate one ormore program components, the one or more program components comprising:an acceptor configured to: receive a set of cardiac electrical signals;receive an indication of a measurement location corresponding to each ofthe set of electrical signals; and an annotation waveform generatorconfigured to: identify, for each electrical signal of the set ofelectrical signals, a deflection, the deflection comprising a deviationfrom a signal baseline; and generate, based on the identifieddeflections, an activation waveform corresponding to the set ofelectrical signals.

While multiple embodiments are disclosed, still other embodiments of thepresently disclosed subject matter will become apparent to those skilledin the art from the following detailed description, which shows anddescribes illustrative embodiments of the disclosed subject matter.Accordingly, the drawings and detailed description are to be regarded asillustrative in nature and not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual schematic diagram depicting an illustrativecardiac mapping system, in accordance with embodiments of the subjectmatter disclosed herein.

FIG. 2 is a block diagram depicting an illustrative processing unit foruse with a cardiac mapping system, in accordance with embodiments of thesubject matter disclosed herein.

FIG. 3 is a flow diagram depicting an illustrative process forgenerating a cardiac map, in accordance with embodiments of the subjectmatter disclosed herein.

FIG. 4 is a flow diagram depicting an illustrative method of processingelectrophysiological information, in accordance with embodiments of thesubject matter disclosed herein.

FIG. 5A depicts an illustrative graphical representation of electricalsignals received from a mapping catheter, in accordance with embodimentsof the subject matter disclosed herein.

FIG. 5B depicts an activation waveform corresponding to the illustrativegraphical representation of electrical signals depicted in FIG. 5A, inaccordance with embodiments of the subject matter disclosed herein.

FIG. 5C depicts an illustrative graphical representation of theactivation waveform depicted in FIG. 5B, in accordance with embodimentsof the subject matter disclosed herein.

FIG. 5D depicts an illustrative graphical representation of a filteredactivation waveform based on the activation waveform depicted in FIGS.5B and 5C, in accordance with embodiments of the subject matterdisclosed herein.

While the disclosed subject matter is amenable to various modificationsand alternative forms, specific embodiments have been shown by way ofexample in the drawings and are described in detail below. Theintention, however, is not to limit the disclosure to the particularembodiments described. On the contrary, the disclosure is intended tocover all modifications, equivalents, and alternatives falling withinthe scope of the disclosure as defined by the appended claims.

As the terms are used herein with respect to measurements (e.g.,dimensions, characteristics, attributes, components, etc.), and rangesthereof, of tangible things (e.g., products, inventory, etc.) and/orintangible things (e.g., data, electronic representations of currency,accounts, information, portions of things (e.g., percentages,fractions), calculations, data models, dynamic system models,algorithms, parameters, etc.), “about” and “approximately” may be used,interchangeably, to refer to a measurement that includes the statedmeasurement and that also includes any measurements that are reasonablyclose to the stated measurement, but that may differ by a reasonablysmall amount such as will be understood, and readily ascertained, byindividuals having ordinary skill in the relevant arts to beattributable to measurement error; differences in measurement and/ormanufacturing equipment calibration; human error in reading and/orsetting measurements; adjustments made to optimize performance and/orstructural parameters in view of other measurements (e.g., measurementsassociated with other things); particular implementation scenarios;imprecise adjustment and/or manipulation of things, settings, and/ormeasurements by a person, a computing device, and/or a machine; systemtolerances; control loops; machine-learning; foreseeable variations(e.g., statistically insignificant variations, chaotic variations,system and/or model instabilities, etc.); preferences; and/or the like.

Although the term “block” may be used herein to connote differentelements illustratively employed, the term should not be interpreted asimplying any requirement of, or particular order among or between,various blocks disclosed herein. Similarly, although illustrativemethods may be represented by one or more drawings (e.g., flow diagrams,communication flows, etc.), the drawings should not be interpreted asimplying any requirement of, or particular order among or between,various steps disclosed herein. However, certain embodiments may requirecertain steps and/or certain orders between certain steps, as may beexplicitly described herein and/or as may be understood from the natureof the steps themselves (e.g., the performance of some steps may dependon the outcome of a previous step). Additionally, a “set,” “subset,” or“group” of items (e.g., inputs, algorithms, data values, etc.) mayinclude one or more items, and, similarly, a subset or subgroup of itemsmay include one or more items. A “plurality” means more than one.

As used herein, the term “based on” is not meant to be restrictive, butrather indicates that a determination, identification, prediction,calculation, and/or the like, is performed by using, at least, the termfollowing “based on” as an input. For example, predicting an outcomebased on a particular piece of information may additionally, oralternatively, base the same determination on another piece ofinformation.

DETAILED DESCRIPTION

Embodiments of systems and methods described herein facilitateprocessing sensed cardiac electrical signals to return the per-sample“probability” of tissue activation by generating activation waveforms.An activation waveform is a set of activation waveform values and mayinclude, for example, a set of discrete activation waveform values(e.g., a set of activation waveform values, a set of activation timeannotations, etc.), a function defining an activation waveform curve,and/or the like. Accordingly, in embodiments, the term “activationwaveform” may include a “filtered activation waveform,” as describedbelow. Similarly, as explained herein, embodiments of systems andmethods described herein facilitate generating other types of annotationwaveforms. An annotation waveform is a set of annotation waveform valuesand may include, for example, a set of discrete activation annotationvalues (e.g., a set of annotation waveform values, a set of timeannotations, etc.), a function defining an annotation waveform curve,and/or the like. Accordingly, in embodiments, the term “annotationwaveform” may include a “filtered annotation waveform.” Although much ofthe description herein relates to activation waveforms and activationhistograms, this is only for the purpose of clarity of description, andit is to be understood that any number of different aspects ofembodiments described in relation to activation waveforms and/oractivation histograms may apply more generally to annotation waveformsand/or annotation histograms, respectively.

Embodiments facilitate finding meaningful deflections while rejectingnoises and artifacts. In embodiments, the annotation waveform may bedisplayed, used to present an activation waveform propagation map, usedto facilitate diagnoses, used to facilitate classification of electricalsignals, and/or the like. According to embodiments, to perform aspectsof embodiments of the methods described herein, the cardiac electricalsignals may be obtained from a mapping catheter (e.g., associated with amapping system), a recording system, a coronary sinus (CS) catheter orother reference catheter, an ablation catheter, a memory device (e.g., alocal memory, a cloud server, etc.), a communication component, amedical device (e.g., an implantable medical device, an external medicaldevice, a telemetry device, etc.), and/or the like.

As the term is used herein, a sensed cardiac electrical signal may referto one or more sensed signals. Each cardiac electrical signal mayinclude a number of intracardiac electrograms (EGMs) sensed within apatient's heart, and may include any number of features that may beascertained by aspects of the system 100. Examples of cardiac electricalsignal features include, but are not limited to, activation times,activations, activation waveforms, filtered activation waveforms,minimum voltage values, maximum voltages values, maximum negativetime-derivatives of voltages, instantaneous potentials, voltageamplitudes, dominant frequencies, peak-to-peak voltages, and/or thelike. A cardiac electrical signal feature may refer to one or morefeatures extracted from one or more cardiac electrical signals, derivedfrom one or more features that are extracted from one or more cardiacelectrical signals, and/or the like. Additionally, a representation, ona cardiac and/or a surface map, of a cardiac electrical signal featuremay represent one or more cardiac electrical signal features, aninterpolation of a number of cardiac electrical signal features, and/orthe like.

Each cardiac signal also may be associated with a set of respectiveposition coordinates that corresponds to the location at which thecardiac electrical signal was sensed. Each of the respective positioncoordinates for the sensed cardiac signals may include three-dimensionalCartesian coordinates, polar coordinates, and/or the like. Inembodiments, other coordinate systems can be used. In embodiments, anarbitrary origin is used and the respective position coordinates referto positions in space relative to the arbitrary origin. Since, inembodiments, the cardiac signals may be sensed on the cardiac surfaces,the respective position coordinates may be on the endocardial surface,epicardial surface, in the mid-myocardium of the patient's heart, and/orin the vicinity of one of one of these.

FIG. 1 shows a schematic diagram of an exemplary embodiment of a cardiacmapping system 100. As indicated above, embodiments of the subjectmatter disclosed herein may be implemented in a mapping system (e.g.,the mapping system 100), while other embodiments may be implemented inan ablation system, a recording system, a computer analysis system,and/or the like. The mapping system 100 includes a moveable catheter 110having multiple spatially distributed electrodes. During asignal-acquisition stage of a cardiac mapping procedure, the catheter110 is displaced to multiple locations within the heart chamber intowhich the catheter 110 is inserted. In some embodiments the distal endof the catheter 110 is fitted with multiple electrodes spread somewhatuniformly over the catheter. For example, the electrodes may be mountedon the catheter 110 following a 3D olive shape, a basket shape, and/orthe like. The electrodes are mounted on a device capable of deployingthe electrodes into the desired shape while inside the heart, andretracting the electrodes when the catheter is removed from the heart.To allow deployment into a 3D shape in the heart, electrodes may bemounted on a balloon, shape memory material such as Nitinol, actuablehinged structure, and/or the like. According to embodiments, thecatheter 110 may be a mapping catheter, an ablation catheter, adiagnostic catheter, a CS catheter, and/or the like. For example,aspects of embodiments of the catheter 110, the electrical signalsobtained using the catheter 110, and subsequent processing of theelectrical signals, as described herein, may also be applicable inimplementations having a recording system, ablation system, and/or anyother system having a catheter with electrodes that may be configured toobtain cardiac electrical signals.

At each of the locations to which the catheter 110 is moved, thecatheter's multiple electrodes acquire signals resulting from theelectrical activity in the heart. Consequently, reconstructing andpresenting to a user (such as a doctor and/or technician) physiologicaldata pertaining to the heart's electrical activity may be based oninformation acquired at multiple locations, thereby providing a moreaccurate and faithful reconstruction of physiological behavior of theendocardium surface. The acquisition of signals at multiple catheterlocations in the heart chamber enables the catheter to effectively actas a “mega-catheter” whose effective number of electrodes and electrodespan is proportional to the product of the number of locations in whichsignal acquisition is performed and the number of electrodes thecatheter has.

To enhance the quality of the reconstructed physiological information atthe endocardium surface, in some embodiments the catheter 110 is movedto more than three locations (for example, more than 5, 10, or even 50locations) within the heart chamber. Further, the spatial range overwhich the catheter is moved may be larger than one third (⅓) of thediameter of the heart cavity (for example, larger than 35%, 40%, 50% oreven 60% of the diameter of the heart cavity). Additionally, in someembodiments the reconstructed physiological information is computedbased on signals measured over several heart beats, either at a singlecatheter location within the heart chamber or over several locations. Incircumstances where the reconstructed physiological information is basedon multiple measurements over several heart beats, the measurements maybe synchronized with one another so that the measurement are performedat approximately the same phase of the heart cycle. The signalmeasurements over multiple beats may be synchronized based on featuresdetected from physiological data such as surface electrocardiograms(ECGs) and/or intracardiac electrograms (EGMs).

The cardiac mapping system 100 further includes a processing unit 120which performs several of the operations pertaining to the mappingprocedure, including the reconstruction procedure to determine thephysiological information at the endocardium surface (e.g., as describedabove) and/or within a heart chamber. The processing unit 120 also mayperform a catheter registration procedure. The processing unit 120 alsomay generate a 3D grid used to aggregate the information captured by thecatheter 110 and to facilitate display of portions of that information.

The location of the catheter 110 inserted into the heart chamber can bedetermined using a conventional sensing and tracking system 180 thatprovides the 3D spatial coordinates of the catheter and/or its multipleelectrodes with respect to the catheter's coordinate system asestablished by the sensing and tracking system. These 3D spatiallocations may be used in building the 3D grid. Embodiments of the system100 may use a hybrid location technology that combines impedancelocation with magnetic location technology. This combination may enablethe system 100 to accurately track catheters that are connected to thesystem 100. Magnetic location technology uses magnetic fields generatedby a localization generator positioned under the patient table to trackcatheters with magnetic sensors. Impedance location technology may beused to track catheters that may not be equipped with a magneticlocation sensor, and may utilize surface ECG patches.

In embodiments, to perform a mapping procedure and reconstructphysiological information on the endocardium surface, the processingunit 120 may align the coordinate system of the catheter 110 with theendocardium surface's coordinate system. The processing unit 110 (orsome other processing component of the system 100) may determine acoordinate system transformation function that transforms the 3D spatialcoordinates of the catheter's locations into coordinates expressed interms of the endocardium surface's coordinate system, and/or vice-versa.In embodiments, such a transformation may not be necessary, asembodiments of the 3D grid described herein may be used to capturecontact and non-contact EGMs, and select mapping values based onstatistical distributions associated with nodes of the 3D grid. Theprocessing unit 120 also may perform post-processing operations on thephysiological information to extract and display useful features of theinformation to the operator of the system 100 and/or other persons(e.g., a physician).

According to embodiments, the signals acquired by the multipleelectrodes of catheter 110 are passed to the processing unit 120 via anelectrical module 140, which may include, for example, a signalconditioning component. The electrical module 140 receives the signalscommunicated from the catheter 110 and performs signal enhancementoperations on the signals before they are forwarded to the processingunit 120. The electrical module 140 may include signal conditioninghardware, software, and/or firmware that may be used to amplify, filterand/or sample intracardiac potential measured by one or more electrodes.The intracardiac signals typically have a maximum amplitude of 60 mV,with a mean of a few millivolts.

In some embodiments the signals are bandpass filtered in a frequencyrange (e.g., 0.5-500 Hz) and sampled with analog to digital converters(e.g., with 15-bit resolution at 1 kHz). To avoid interference withelectrical equipment in the room, the signal may be filtered to removethe frequency corresponding to the power supply (e.g., 60 Hz). Othertypes of signal processing operations such as spectral equalization,automatic gain control, etc. may also take place. For example, inembodiments, the intracardiac signals may be unipolar signals, measuredrelative to a reference (which may be a virtual reference) such as, forexample, a coronary sinus catheter or Wilson's Central Terminal (WCT),from which the signal processing operations may compute differences togenerate multipolar signals (e.g., bipolar signals, tripolar signals,etc.). The signals may be otherwise processed (e.g., filtered, sampled,etc.) before and/or after generating the multipolar signals. Theresultant processed signals are forwarded by the module 140 to theprocessing unit 120 for further processing.

As further shown in FIG. 1, the cardiac mapping system 100 also mayinclude peripheral devices such as a printer 150 and/or display device170, both of which may be interconnected to the processing unit 120.Additionally, the mapping system 100 includes storage device 160 thatmay be used to store data acquired by the various interconnectedmodules, including the volumetric images, raw data measured byelectrodes and/or the resultant endocardium representation computedtherefrom, the partially computed transformations used to expedite themapping procedures, the reconstructed physiological informationcorresponding to the endocardium surface, and/or the like.

In embodiments, the processing unit 120 may be configured toautomatically improve the accuracy of its algorithms by using one ormore artificial intelligence (i.e., machine-learning) techniques,classifiers, and/or the like. In embodiments, for example, theprocessing unit may use one or more supervised and/or unsupervisedtechniques such as, for example, support vector machines (SVMs),k-nearest neighbor techniques, artificial neural networks, and/or thelike. In embodiments, classifiers may be trained and/or adapted usingfeedback information from a user, other metrics, and/or the like.

The illustrative cardiac mapping system 100 shown in FIG. 1 is notintended to suggest any limitation as to the scope of use orfunctionality of embodiments of the present disclosure. Neither shouldthe illustrative cardiac mapping system 100 be interpreted as having anydependency or requirement related to any single component or combinationof components illustrated therein. Additionally, various componentsdepicted in FIG. 1 may be, in embodiments, integrated with various onesof the other components depicted therein (and/or components notillustrated), all of which are considered to be within the ambit of thesubject matter disclosed herein. For example, the electrical module 140may be integrated with the processing unit 120. Additionally, oralternatively, aspects of embodiments of the cardiac mapping system 100may be implemented in a computer analysis system configured to receivecardiac electrical signals and/or other information from a memory device(e.g., a cloud server, a mapping system memory, etc.), and performaspects of embodiments of the methods described herein for processingcardiac information (e.g., determining annotation waveforms, etc.). Thatis, for example, a computer analysis system may include a processingunit 120, but not a mapping catheter.

FIG. 2 is a block diagram of an illustrative processing unit 200, inaccordance with embodiments of the disclosure. The processing unit 200may be, be similar to, include, or be included in the processing unit120 depicted in FIG. 1. As shown in FIG. 2, the processing unit 200 maybe implemented on a computing device that includes a processor 202 and amemory 204. Although the processing unit 200 is referred to herein inthe singular, the processing unit 200 may be implemented in multipleinstances (e.g., as a server cluster), distributed across multiplecomputing devices, instantiated within multiple virtual machines, and/orthe like. One or more components for facilitating cardiac mapping may bestored in the memory 204. In embodiments, the processor 202 may beconfigured to instantiate the one or more components to generate anannotation waveform 206, an annotation histogram 208, and a cardiac map210, any one or more of which may be stored in the memory 204.

As is further depicted in FIG. 2, the processing unit 200 may include anacceptor 212 configured to receive electrical signals from a mappingcatheter (e.g., the mapping catheter 110 depicted in FIG. 1). Themeasured electrical signals may include a number of intracardiacelectrograms (EGMs) sensed within a patient's heart. The acceptor 212may also receive an indication of a measurement location correspondingto each of the electrical signals. In embodiments, the acceptor 212 maybe configured to determine whether to accept the electrical signals thathave been received. The acceptor 212 may utilize any number of differentcomponents and/or techniques to determine which electrical signals orbeats to accept, such as filtering, beat matching, morphology analysis,positional information (e.g., catheter motion), respiration gating,and/or the like.

The accepted electrical signals are received by an annotation waveformgenerator 214 that is configured to extract at least one annotationfeature from each of the electrical signals, in cases in which theelectrical signal includes an annotation feature to extract. Inembodiments, the at least one annotation feature includes at least onevalue corresponding to at least one annotation metric. The at least onefeature may include at least one event, where the at least one eventincludes the at least one value corresponding to the at least one metricand/or at least one corresponding time (a corresponding time does notnecessarily exist for each annotation feature). According toembodiments, the at least one metric may include, for example, anactivation time, minimum voltage value, maximum voltage value, maximumnegative time-derivative of voltage, an instantaneous potential, avoltage amplitude, a dominant frequency, a peak-to-peak voltage, anactivation duration, and/or the like. According to embodiments, theannotation waveform generator 214 may be configured to detectactivations and to generate an annotation waveform 206, which may be,for example, an activation waveform.

As shown in FIG. 2, the processing unit 200 includes a histogramgenerator 216 that is configured to generate an annotation histogram 208having a number of bins within which annotations from electrograms(EGMs) are included. The processing unit 200, using the histogramgenerator 216, may be configured to aggregate a set of annotationfeatures by including each of the features and/or EGMs in a histogram.For example, the histogram generator 216 may be configured to aggregatethe set of activation features by assigning a confidence level to eachevent corresponding to an activation feature; determining a weightedconfidence level associated with each event; and including the weightedconfidence levels in a histogram. The processing unit includes anelectrogram (EGM) classifier 218 that is configured to classify EGMsaccording to any number of different classifications based, for example,on characteristic of the EGM, the annotation waveform 206, annotationhistogram 208, and/or the like. Additionally, the processing unit 200includes a mapping engine 220 that is configured to facilitatepresentation of a map 210 corresponding to a cardiac surface based onthe electrical signals. In embodiments, the map 210 may include avoltage map, an activation map, a fractionation map, velocity map,confidence map, and/or the like.

The illustrative processing unit 200 shown in FIG. 2 is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments of the present disclosure. Neither should the illustrativeprocessing unit 200 be interpreted as having any dependency orrequirement related to any single component or combination of componentsillustrated therein. Additionally, any one or more of the componentsdepicted in FIG. 2 may be, in embodiments, integrated with various onesof the other components depicted therein (and/or components notillustrated), all of which are considered to be within the ambit of thesubject matter disclosed herein. For example, the acceptor 212 may beintegrated with the EGM classifier 218 and/or the mapping engine 220. Inembodiments, the processing unit 200 may not include an acceptor 212,while in other embodiments, the acceptor 212 may be configured toreceive electrical signals from a memory device, a communicationcomponent, and/or the like.

Additionally, the processing unit 200 may (alone and/or in combinationwith other components of the system 100 depicted in FIG. 1, and/or othercomponents not illustrated) perform any number of different functionsand/or processes associated with cardiac mapping (e.g., triggering,blanking, field mapping, etc.) such as, for example, those described inU.S. Pat. No. 8,428,700, entitled “ELECTROANATOMICAL MAPPING;” U.S. Pat.No. 8,948,837, entitled “ELECTROANATOMICAL MAPPING;” U.S. Pat. No.8,615,287, entitled “CATHETER TRACKING AND ENDOCARDIUM REPRESENTATIONGENERATION;” U.S. Patent Publication 2015/0065836, entitled “ESTIMATINGTHE PREVALENCE OF ACTIVATION PATTERNS IN DATA SEGMENTS DURINGELECTROPHYSIOLOGY MAPPING;” U.S. Pat. No. 6,070,094, entitled “SYSTEMSAND METHODS FOR GUIDING MOVABLE ELECTRODE ELEMENTS WITHINMULTIPLE-ELECTRODE STRUCTURE;” U.S. Pat. No. 6,233,491, entitled“CARDIAC MAPPING AND ABLATION SYSTEMS;” U.S. Pat. No. 6,735,465,entitled “SYSTEMS AND PROCESSES FOR REFINING A REGISTERED MAP OF A BODYCAVITY;” the disclosures of which are hereby expressly incorporatedherein by reference.

According to embodiments, various components of the mapping system 100,illustrated in FIG. 1, and/or the processing unit 200, illustrated inFIG. 2, may be implemented on one or more computing devices. A computingdevice may include any type of computing device suitable forimplementing embodiments of the disclosure. Examples of computingdevices include specialized computing devices or general-purposecomputing devices such “workstations,” “servers,” “laptops,” “desktops,”“tablet computers,” “hand-held devices,” “general-purpose graphicsprocessing units (GPGPUs),” and the like, all of which are contemplatedwithin the scope of FIGS. 1 and 2 with reference to various componentsof the system 100 and/or processing unit 200.

In embodiments, a computing device includes a bus that, directly and/orindirectly, couples the following devices: a processor, a memory, aninput/output (I/O) port, an I/O component, and a power supply. Anynumber of additional components, different components, and/orcombinations of components may also be included in the computing device.The bus represents what may be one or more busses (such as, for example,an address bus, data bus, or combination thereof). Similarly, inembodiments, the computing device may include a number of processors, anumber of memory components, a number of I/O ports, a number of I/Ocomponents, and/or a number of power supplies. Additionally any numberof these components, or combinations thereof, may be distributed and/orduplicated across a number of computing devices.

In embodiments, memory (e.g., the storage device 160 depicted in FIG. 1,and/or the memory 204 depicted in FIG. 2) includes computer-readablemedia in the form of volatile and/or nonvolatile memory and may beremovable, nonremovable, or a combination thereof. Media examplesinclude Random Access Memory (RAM); Read Only Memory (ROM);Electronically Erasable Programmable Read Only Memory (EEPROM); flashmemory; optical or holographic media; magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices; datatransmissions; and/or any other medium that can be used to storeinformation and can be accessed by a computing device such as, forexample, quantum state memory, and/or the like. In embodiments, thememory 160 and/or 204 stores computer-executable instructions forcausing a processor (e.g., the processing unit 120 depicted in FIG. 1and/or the processor 202 depicted in FIG. 2) to implement aspects ofembodiments of system components discussed herein and/or to performaspects of embodiments of methods and procedures discussed herein.

Computer-executable instructions may include, for example, computercode, machine-useable instructions, and the like such as, for example,program components capable of being executed by one or more processorsassociated with a computing device. Examples of such program componentsinclude the annotation waveform 206, the annotation histogram 208, themap 210, the acceptor 212, the annotation waveform generator 214, thehistogram generator 216, the EGM classifier 218, and the mapping engine220. Program components may be programmed using any number of differentprogramming environments, including various languages, development kits,frameworks, and/or the like. Some or all of the functionalitycontemplated herein may also, or alternatively, be implemented inhardware and/or firmware.

FIG. 3 is a flow diagram of an illustrative process 300 for automatedelectro-anatomical mapping, in accordance with embodiments of thedisclosure. Aspects of embodiments of the method 300 may be performed,for example, by a processing unit (e.g., the processing unit 120depicted in FIG. 1, and/or the processing unit 200 depicted in FIG. 2).A data stream 302 containing multiple signals is first input into thesystem (e.g., the cardiac mapping system 100 depicted in FIG. 1). Duringthe automated electro-anatomical mapping process, a data stream 302provides a collection of physiological and non-physiological signalsthat serve as inputs to the mapping process. The signals may becollected directly by the mapping system, and/or obtained from anothersystem using an analog or digital interface. The data stream 302 mayinclude signals such as unipolar and/or bipolar intracardiacelectrograms (EGMs), surface electrocardiograms (ECGs), electrodelocation information originating from one or more of a variety ofmethodologies (magnetic, impedance, ultrasound, real time MRI, etc.),tissue proximity information, catheter force and/or contact informationobtained from one or more of a variety of methodologies (force springsensing, piezo-electric sensing, optical sensing etc.), catheter tipand/or tissue temperature, acoustic information, catheter electricalcoupling information, catheter deployment shape information, electrodeproperties, respiration phase, blood pressure, other physiologicalinformation, and/or the like.

For the generation of specific types of maps, one or more signals may beused as one or more references, during a triggering/alignment process304, to trigger and align the data stream 302 relative to the cardiac,other biological cycle and/or an asynchronous system clock resulting inbeat datasets. Additionally, for each incoming beat dataset, a number ofbeat metrics are computed during a beat metric determination process306. Beat metrics may be computed using information from a singlesignal, spanning multiple signals within the same beat and/or fromsignals spanning multiple beats. The beat metrics provide multiple typesof information on the quality of the specific beat dataset and/orlikelihood that the beat data is good for inclusion in the map dataset.A beat acceptance process 308 aggregates the criteria and determineswhich beat datasets will make up the map dataset 310. The map dataset310 may be stored in association with a 3D grid that is dynamicallygenerated during data acquisition.

Surface geometry data may be generated concurrently during the same dataacquisition process using identical and/or different triggering and/orbeat acceptance metrics employing a surface geometry constructionprocess 312. This process constructs surface geometry using data such aselectrode locations and catheter shape contained in the data stream.Additionally, or alternatively, previously collected surface geometry316 may be used as an input to surface geometry data 318. Such geometrymay have been collected previously in the same procedure using adifferent map dataset, and/or using a different modality such as CT,MRI, ultrasound, rotational angiography, and/or the like, and registeredto the catheter locating system. The system performs a source selectionprocess 314, in which it selects the source of the surface geometry dataand provides surface geometry data 318 to a surface map generationprocess 320. The surface map generation process 320 is employed togenerate surface map data 322 from the map dataset 310 and surfacegeometry data 318.

The surface geometry construction algorithm generates the anatomicalsurface on which the electroanatomical map is displayed. Surfacegeometry can be constructed, for example, using aspects of a system asdescribed U.S. patent application Ser. No. 12/437,794, entitled“Impedance Based Anatomy Generation” and filed on May 8, 2008; and/orU.S. Pat. No. 8,948,837, entitled “Electroanatomical Mapping” and issuedon Feb. 3, 2015, the contents of each of which is incorporated byreference herein in its entirety. Additionally, or alternatively, ananatomical shell can be constructed by the processing unit by fitting asurface on electrode locations that are determined either by the user orautomatically to be on the surface of the chamber. In addition, asurface can be fit on the outermost electrode and/or catheter locationswithin the chamber.

As described, the map dataset 310 from which the surface is constructedcan employ identical or different beat acceptance criteria from thoseused for electrical and other types of maps. The map dataset 310 forsurface geometry construction can be collected concurrently withelectrical data or separately. Surface geometry can be represented as amesh containing a collection of vertices (points) and the connectivitybetween them (e.g. triangles). Alternatively, surface geometry can berepresented by different functions such as higher order meshes,non-uniform rational basis splines (NURBS), and/or curvilinear shapes.

The generation process 320 generates surface map data 322. The surfacemap data 322 may provide information on cardiac electrical excitation,cardiac motion, tissue proximity information, tissue impedanceinformation, force information, and/or any other collected informationdesirable to the clinician. The combination of map dataset 310 andsurface geometry data 318 allows for surface map generation. The surfacemap is a collection of values or waveforms (e.g., EGMs) on the surfaceof the chamber of interest, whereas the map dataset can contain datathat is not on the cardiac surface. One approach for processing the mapdataset 310 and surface geometry data 318 to obtain a surface mapdataset 322 is described in U.S. Pat. No. 7,515,954, entitled“NON-CONTACT CARDIAC MAPPING, INCLUDING MOVING CATHETER AND MULTI-BEATINTEGRATION” and filed Jun. 13, 2006, the contents of which isincorporated by reference herein in its entirety.

Alternatively, or in combination with the method above, an algorithmthat applies acceptance criteria to individual electrodes can beemployed. For example, electrode locations exceeding a set distance(e.g., 3 mm) from surface geometry can be rejected. Another algorithmcan incorporate tissue proximity information using impedance forinclusion in the surface map data. In this case only electrode locationwhose proximity value is less than 3 mm might be included. Additionalmetrics of the underlying data can also be used for this purpose. Forexample, EGM properties similar to beat metrics can be assessed on a perelectrode basis. In this case metrics such as far field overlap and/orEGM consistency can be used. It should be understood that variations onthe method to project points from the map dataset 310 to the surfaceand/or to select appropriate points can exist.

Once obtained, the surface map data 322 may be further processed toannotate desired features from the underlying data, a process defined assurface map annotation 324. Once data is collected into surface map data322, attributes relating to the collected data may be automaticallypresented to the user. These attributes can be automatically determinedand applied to the data by the computer system and are referred toherein as annotations. Exemplary annotations include activation time,the presence of double activation or fractionation, voltage amplitude,spectral content, and/or the like. Due to the abundance of dataavailable in automated mapping (e.g., mapping completed by the computersystem with minimal human input related to the incoming data), it is notpractical for the operator to review and annotate data manually.However, human input can be a valuable addition to the data, and so whenuser input is provided it is necessary for the computer system toautomatically propagate and apply it to more than one data point at atime.

It may be possible to use the computer system to automatically annotateactivation time, voltage, and other characteristics of individual EGMs.Activation time detection may use methods similar to those previouslydescribed to detect a trigger and can similarly benefit from the use ofblanking and powered triggering operator. Desired annotations mayinclude instantaneous potential, activation time, voltage amplitude,dominant frequency and/or other properties of the signal. Once computed,the annotations may be displayed superimposed on chamber geometry. Inembodiments, a gap-filling surface map interpolation may be employed326. For example, in embodiments, a gap-filling interpolation may beemployed where a distance between a point on the surface to a measuredEGM exceeds a threshold, as this may indicate, for example, thatgrid-based interpolation, as described herein, may not be as effectivein that situation. Displayed maps 328 can be computed and displayedseparately, and/or overlaid on top of each other.

The illustrative process 300 shown in FIG. 3 is not intended to suggestany limitation as to the scope of use or functionality of embodiments ofthe present disclosure. Neither should the illustrative process 300 beinterpreted as having any dependency or requirement related to anysingle component or combination of components illustrated therein.Additionally, any one or more of the components depicted in FIG. 3 maybe, in embodiments, integrated with various ones of the other componentsdepicted therein (and/or components not illustrated), all of which areconsidered to be within the ambit of the present disclosure.

FIG. 4 is a flow diagram depicting an illustrative method 400 ofprocessing electrophysiological information, in accordance withembodiments of the disclosure. Aspects of embodiments of the method 400may be performed, for example, by a processing unit (e.g., theprocessing unit 120 depicted in FIG. 1, and/or the processing unit 200depicted in FIG. 2). Embodiments of the method 400 include receiving aplurality of electrical signals from a catheter (block 402). Thecatheter may be any catheter having one or more electrodes configured toobtain electrical signals (e.g., the mapping catheter 110 depicted inFIG. 1, a CS catheter, an ablation catheter, etc.). The processing unitalso may receive an indication of a measurement location correspondingto each of the electrical signals. As explained above, with respect toFIG. 3, the processing unit and/or other components (e.g., theelectrical module 140 depicted in FIG. 1) may be configured to determinewhether to accept particular electrical signals (e.g., beats) based onone or more beat acceptance criteria.

According to embodiments, cardiac electric signal features may beextracted from the cardiac electrical signals (e.g., EGMs). Examples offeatures of the cardiac electrical signals include, but are not limitedto, activation times, minimum voltage values, maximum voltages values,maximum negative time-derivatives of voltages, instantaneous potentials,voltage amplitudes, dominant frequencies, peak-to-peak voltages, and/orthe like. Each of the respective points at which a cardiac electricalsignal is sensed may have a corresponding set of three-dimensionalposition coordinates. For example, the position coordinates of thepoints may be represented in Cartesian coordinates. Other coordinatesystems can be used, as well. In embodiments, an arbitrary origin isused and the respective position coordinates are defined with respect tothe arbitrary origin. In some embodiments, the points have non-uniformspacing, while in other embodiments, the points have uniform spacing. Inembodiments, the point corresponding to each sensed cardiac electricalsignal may be located on the endocardial surface of the heart and/orbelow the endocardial surface of the heart.

FIG. 5A shows an exemplary graphical representation 500 illustratingelectrical signals (in this case, EGMs) received from a mappingcatheter, each representing a magnitude of a depolarization sequence ofa heart during a predetermined time period. In this example, a firstsignal U0 may be associated with a first electrode G4, a second signalU1 may be associated with a second electrode G5, and a third signal U2may be associated with a third electrode G6. The signals U0, U1, U2 mayrepresent unipolar signals received from a first mapping catheter and afourth signal B may represent a bipolar signal associated with thesecond and third electrodes G5 and G6. In embodiments, the bipolarsignal B may represent a graphical summation of signals received fromthe second and third electrodes G5 and G6. In embodiments, and, forexample, depending on a lead tip configuration of a catheter, theacquired electrical signals may be unipolar signals, bipolar signals,and/or other multipolar signals.

With continuing reference to FIG. 4, to filter out unwanted signals,embodiments of the method 400 also include rejecting or attenuatingfar-field signal components from the acquired electrical signals (block404). For example, R-waves sensed by an atrial channel of the heart thatare unrelated to the diagnostic assessment may be rejected or attenuatedas unwanted signals. Other exemplary unwanted signals may include commonmode noise such as power-line noise, T-waves, skeletal musclemyopotentials (e.g., from a pectoral muscle), and crosstalk signals fromanother device (e.g., a pacemaker) to suit different applications. Inembodiments, far-field signal components may be rejected or attenuatedby creating a multipolar signal (e.g., the bipolar signal B associatedwith the second and third electrodes G5 and G6, depicted in FIG. 5A, atripolar signal, etc.). Any number of other techniques may beimplemented for processing the electrical signals such as, for example,signal conditioning, filtering, transforming, and/or the like. Inembodiments, the method 400 may include any number of other types ofartifact rejection. For example, in embodiments, artifact rejection maybe achieved using spatio-temporal analysis (e.g., as described belowwith reference to block 410), morphological analysis, continuous-modeartifact rejection, (e.g., unipolar rejection), combinations of unipolarand/or multipolar signals (e.g., nonlinear combination of all receivedsignals), and/or the like. Any number of other types of filtering may beperformed on the electrical signals. As such, for example, the termelectrical signal may also include filtered electrical signals (e.g., asused in connection with subsequent processing steps).

Embodiments of the method 400 include identifying far-field signalcomponents, but not necessarily rejecting or attenuating the identifiedfar-field signal components. In embodiments, for example, far-fieldsignal components may include information that can be used for variousaspects of analyses of cardiac electrical signals. For example,far-field signal components may include information about neighboringanatomical structures. Far-field signal components may be identified,isolated, analyzed, and/or the like. In embodiments, the method 400 mayinclude identifying far-field signal components, and taking some actionin response thereto. That is, for example, in embodiments, far-fieldsignal components may be used in conjunction with detection of local,spatially-varying activation for identifying tachycardia-sustainingisthmus, and/or the like.

Embodiments of the method 400 further include determining a signalbaseline (block 406) during a quiescent time period. The signalbaseline, which may be determined based on historical information,population information, patient information, environmental information,and/or the like, may include a value or range of values determined suchthat deflections of the EGM deviating beyond the baseline by a specifiedamount are deemed to be activations. The signal baseline may be generic,patient-specific, EGM-specific, time-varying, and/or the like. Inembodiments, the signal baseline may be a pre-determined minimum valueand/or maximum value.

In embodiments, a signal baseline may be determined such thatdeflections deviating beyond the signal baseline have some computedprobability (or minimum probability) of being activations. According toembodiments, the signal baseline may include a range of values thatrepresent, and/or are determined based on a noise floor. That is, forexample, a noise floor may be estimated and the signal baselineestablished as the upper and lower boundaries of the noise floor, amultiple of the noise floor, and/or some other function of the noisefloor (e.g., within a certain standard deviation of the noise floor,etc.). According to embodiments, for example, a signal baselinedetermination process may include identifying “quiet periods” within aspecified window (e.g., a 0.5 second time window, a 1 second timewindow, etc.), and determining the signal baseline based on the quietperiods. That is, for example, within a specified window, one or moretime periods may be identified in which the amplitude of the electricalsignal is within a specified range, and/or within a specified distancefrom other amplitudes. An electrical signal may be an electrogram (EGM),a filtered EGM, a set of absolute values of an EGM, values of peaks ofan EGM at peak locations, a combination of these, and/or the like. Forexample, an electrical signal may be represented as a set of orderedvalues (e.g., the amplitude of each sample point may be a value in theset), and a specified percentile and/or multiplier thereof, may be usedto define the signal baseline. That is, for example, a multiplier of the20th percentile (e.g., the 20th lowest value or bin of values) may beused to define the signal baseline. In embodiments, to determine thesignal baseline, one or more electrical signals may be dilated, and thedilated electrical signal (e.g., dilated EGM) may be used to compute the20th percentile. Dilation is an operation that replaces every samplewith the maximum of the samples in a specified time window (e.g., 15 ms,20 ms, 25 ms, etc.). Dilation can also be described, for example, as amoving maximum (similar to a moving average, but in which values arereplaced with the maximum value in the window instead of the averagevalue). Similar analyses may be implemented with regard to any one ormore other characteristics of an electrical signal (e.g., frequency,signal-to-noise ratio (SNR), etc.), and/or the like.

In embodiments, the signal baseline may be determined based onparticular attributes of a specific patient, environmental information,corresponding portions of a cardiac cycle, aspects of a referencesignal, and/or the like. Additionally, or alternatively, the signalbaseline may be determined based on a certain sample of information suchas, for example, information associated with a set of acquired EGMswithin a specified region. The specified region also may be used toidentify the EGMs used in any number of other aspects of embodiments ofthe method 400. The specified region may be defined (e.g., in onedimension (1D), two dimensions (2D), three dimensions (3D), etc.)according to a specified radius. In embodiments, the specified radiusmay be, be similar to, include, be included within, and/or be determinedbased on, a stochastic radius such as is described in U.S. applicationSer. No. 15/230,233, entitled “CARDIAC MAPPING USING A 3D GRID,” filedon Aug. 5, 2016, and which claims the priority benefit of U.S.Provisional Application No. 62/202,711, having the same title, and filedon Aug. 7, 2015, the entirety of each of which is hereby expresslyincorporated herein by reference for all purposes.

In embodiments, the specified region may be defined in the context oftime such as, for example, by defining the specified region to be thespatial region that includes any EGMs recorded during a specified timeperiod (e.g., during the window of analysis). Any number of differentcombinations of the above characteristics of the specified region may beimplemented and may include any number of other considerations (e.g., aspecified arrhythmia, a specified treatment, a specified medical device,etc.).

In embodiments, the specified region may be predetermined and/or fixed.In embodiments, the specified region may be determined by calculating amaximum distance between two adjacent points on a grid or graph used foraggregating acquired electrical signals, and/or may be configured tooptimize the relevance of aggregate values from the grid and/or graphthat may be interpolated onto an anatomical mesh, aggregate activationinformation associated with activation waveforms, and/or the like. Thespecified region may be defined using any number of differentmeasurements of distance (e.g., a rectilinear distance, L1, a Euclideandistance, L2, etc.), time, relevance (e.g., confidence levels,weightings, etc.).

The specified region may be adaptive and may be dynamically adjustedbased on any number of different factors such as, for example, userinput, mapping quality metrics (e.g., a surface projection distance(SPD), which is the maximum distance that an electrode can fall from amesh surface and still be projected into the map, which may be setand/or adjusted to facilitate control over the accuracy of the map),environmental parameters, physiological parameters, and/or the like.

As is further shown in FIG. 4, embodiments of the method 400 includeidentifying one or more deflections in an electrical signal that deviatebeyond the signal baseline according to one or more specified criteria(block 408). For example, a deflection may be identified wherein theamplitude of the signal exceeds a signal baseline value, wherein theamplitude of the signal deviates beyond baseline by a specified amount(e.g., a relative deviation), and/or the like. In this manner, while theidentification of deflections deviating beyond the signal baselineaccording to one or more specified criteria may have a weak amplitudedependency, this identification is generally not dependent upon, oraffected by, variations in amplitude within ranges based on the baselinesignal.

In embodiments, identifying deflections that deviate beyond the signalbaseline may include determining, for each sample point of an electricalsignal, a corresponding activation waveform value. For example, inembodiments, the method 400 may include determining a probability (e.g.,a value between 0 and 1, inclusive) that a given sample point representsan activation, based on its relation to the signal baseline. Inembodiments, other numerical scales may be used for assigning theprobability such as, for example, values between 0 and 100, and/or thelike. In embodiments, a likelihood (e.g., a probability) that a signaldeflection represents an activation may be determined based on thedeviation of that deflection from the signal baseline. For example, adeflection having a maximum amplitude that deviates from the signalbaseline by at least a specified amount may be assigned a probability of1, while a deflection having a maximum amplitude that deviates from thesignal baseline by at most a specified amount may be assigned aprobability of 0. Probabilities may be assigned, in linear and/ornonlinear, fashions to deflections having amplitudes that are notsatisfied by either of the preceding criteria based on, for example, therelative deviation of the deflection amplitude with respect to the abovecriteria. In this manner, for example, an activation waveform value maybe a probability that an identified deflection corresponding to a samplepoint represents an activation.

According to embodiments, original EGM information during detectedperiods of deviation from baseline may be used to further refine thelikelihood of activation. This information may include, for example, theslope of the EGM, the monotonicity of the EGM (e.g., whether the slopestays positive for 1 ms or for 40 ms before becoming negative), thepresence of adjacent deflections, and/or the like. In embodiments, forexample, detected baseline deviations having a slope close to 0 (e.g.,within a specified range around 0) may have their likelihood scorediminished. Detected baseline deviations that contain a monotonic EGMsignal (e.g., the slope does not change sign) for specified timedurations (e.g., for greater than 11 ms) may have their likelihood scorediminished. In embodiments, detected deviations from baseline that areadjacent or overlapping other deviations from baseline with largeramplitude may have their likelihood score diminished. This may be doneby comparing the prominence of the peak of a deflection to theprominence of the adjacent peaks and diminishing the likelihood score asthis ratio falls. A deflection that fits this description can bevisually described as a shoulder of a larger amplitude deflection.

In embodiments, for example, activation waveform values may representconfidence levels associated with each sample point. That is, forexample, an activation waveform value of 1, or approximately 1, mayindicate a relatively high (e.g., relative to confidence levelsassociated with other values between 0 and 1) level of confidence thatthe corresponding sample point represents a deflection from the signalbaseline due to an activation, while an activation waveform value of 0,or approximately 0, may indicate a relatively low (e.g., relative toconfidence levels associated with other values between 0 and 1) level ofconfidence that the corresponding sample point represents a deflectionfrom the signal baseline due to an activation. In embodiments, theactivation waveform values may be determined using any number ofdifferent statistical models, physiological models, and/or the like.According to embodiments, the calculations (e.g., models, formulas,etc.) used to determine activation waveform values may be configured tominimize dependency on amplitude. In embodiments, the calculations usedto determine activation waveform values may be biased toward generatingactivation waveform values that are either close to (e.g.,approximately) 0 or close to 1. For example, weightings, step-wisefunctions, discrete transforms, and/or the like may be used to bias thedetermination of each activation waveform value toward 0 or 1. In thismanner, a plurality of sample points of an electrical signal may berepresented by a plurality of activation waveform values that form anactivation waveform that has an approximately discrete distribution,thereby facilitating the efficient identification of activations, evenin the case of fractionated EGMs. Accordingly, embodiments mayfacilitate detecting activations, which may facilitate more accurate andefficient mapping, ablating, and/or the like.

In embodiments, an activation waveform value may be determined and/orfurther adjusted based on further analysis such as, for example, resultsof a consistency evaluation, as described below with reference to block410. Any number of other types of information and/or analyses may beincorporated to refine determination of activation waveform values foreach sample point of an electrical signal. In embodiments, one or moremachine-learning techniques (e.g., supervised and/or unsupervisedclassifiers, neural networks, deep learning, artificial reasoning, etc.)may be used to modify aspects of embodiments of the method 400 such as,for example, by enhancing (e.g., making more efficient, accurate, etc.)an activation waveform value calculation formula, and/or the like.

According to embodiments, identification of deflections from thebaseline signal according to specified criteria may includeidentification of potential activations, which may be represented, forexample, using an activation waveform (e.g., the annotation waveform 206depicted in FIG. 2). For example, FIG. 5B shows an exemplary graphicalrepresentation 508 of an activation waveform A having activations 510,512, 514, all activations detected from the electrical signal U1received from the electrode G5 during the predetermined time period. Inembodiments, the activation waveform represents identification ofactivations based on absolute values of deflections that deviate beyonda baseline signal according to one or more specified criteria (e.g.,having an absolute value of a maximum amplitude greater than or equal toa threshold value).

As shown in FIG. 5B, a first activation 510 corresponds to a firstdeflection 502 detected in the signal U1, a second activation 512corresponds to a second deflection 504 detected in the signal U1, and athird activation 514 corresponds to a third deflection 506 detected inthe signal U1. As shown in FIG. 5A, the electrical signals U0, U1, U2may be evaluated during a predetermined time period for the diagnosticassessment. As an example only, an electrical signal having multiplemyocardial capture signals, each having a predetermined amplitude and apredetermined pulse width, may be evaluated to detect activations duringthe predetermined time period. In embodiments, the amplitude of eachactivation represented on the graphical representation of the activationwaveform may correspond to a specified value (e.g., each activation maybe assigned an amplitude of 1), an amplitude (e.g., voltage, currentdensity, etc.) of one or more electrical signals associated with theidentified activation, an aggregated amplitude value corresponding toone or more electrical signals associated with the identified activation(e.g., a mean amplitude, a median amplitude, etc.), and/or the like. Inembodiments, each activation may represent a bin of an activationhistogram, and the amplitude of the activation in the activationhistogram may represent a relative population of the associated bin(e.g., relative to the population of one or more other bins). Anactivation histogram is a histogram constructed from one or moreactivation waveforms. Similarly, an annotation histogram is a histogramconstructed from one or more annotation waveforms.

In embodiments, noise and artifact signals (e.g., deflection 502 insignal U1) may still be included in the waveform A. To remove the noiseand/or artifacts, thereby creating a filtered activation waveform A,embodiments of the method 400 include performing an artifact rejectionagainst the activation waveform based on a spatiotemporal deflectionconsistency between two or more electrical signals (block 410).According to embodiments, consistency may be determined in any number ofdifferent ways. For example, spatiotemporal deflection consistencybetween two electrical signals may refer to the occurrence ofcorresponding identified deflections at approximately the same time,within a specified time window, and/or the like. In embodiments, forexample, a deflection that occurs in less than all of a specified set ofelectrical signals may be rejected as being an artifact. In embodiments,only deflections that are identified as deviating from a signal baselineaccording to one or more criteria are used in the consistencydetermination. In this manner, for example, although a first unipolarEGM may include a deflection that corresponds to a deflection identifiedin another EGM, the deflections may be considered to be inconsistent ifthe first deflection does not deviate beyond the signal baselineaccording to one or more signal criteria. According to embodiments, thestep of evaluating deflection consistency to identify activationsdepicted in block 410 may be, include, be similar to, be included in, orbe otherwise integrated with the step of rejecting far-field signalcomponents depicted in block 404.

Embodiments of the artifact rejection processes described above mayutilize a map and/or a grid that holds beat-gated data collected duringthe same rhythm. The location of the various collected signals may beused to decide whether the information in these signals should be usedfor artifact rejection. In embodiments, the artifact rejection may beaccomplished using techniques similar to those discussed below regarding“continuous” artifact rejection. In embodiments, in contrast to the“continuous” artifact rejection, the methods discussed above may includecomparing a first signal to at least a second signal that was collectedat a different time than the time during which the first signal wascollected. In embodiments, this may include establishing assumptionssuch as, for example, that the data used for artifact rejection wascollected during the same rhythm, that the data used for artifactrejection was collected at the same phase of the cardiac cycle, and/orthe like.

According to embodiments, a “continuous” method of assessingspatiotemporal deflection consistency to detect artifacts may be used.In embodiments of a continuous method, deviations from a baselinesignals may be detected for various combinations of EGMs on a catheter(e.g., all combinations of bipolar and tripolar signals on a channelsuch as, e.g., tripolar signal G4-G5-G6, bipolar signal G4-G5, bipolarsignal G5-G6, bipolar signal G4-G6, etc.). These baseline deviationsignals may be used together to determine whether the observed deviationon any one signal is an activation or an artifact. In embodiments, thiscontinuous method may be configured as a majority rule or votingprocess. In embodiments, the method may be configured as a minimumoperation between the different baseline deviation signals. Inembodiments, this kind of “continuous” artifact rejection comparessimultaneously collected data. It does not require a cardiac map. Thelocations of the signals may be determined from the locationrelationship of the physical electrodes on the catheter.

Activations having inconsistent deflections may be removed or reducedfrom the activation waveform. In this way, only consistent deflectionsmay remain in the activation waveform for examination, thereby reducingthe manual examination time and costs, while facilitating the removal ofnoise and/or artifacts. In embodiments, for example, the plurality ofelectrical signals, such as the first, second, and third signals U0, U1,U2, are compared to one another and/or the activation waveform A fordetecting one or more consistent deflections that are within a range ofpredetermined limits (e.g., minimum and maximum thresholds) relative tothe signal baseline.

FIGS. 5C and 5D show the graphical representation 508 of the activationwaveform A, and an illustrative graphical representation 516 of afiltered activation waveform A, having activations with only consistentdeflections. In FIG. 5D, the graphical representation 516 is overlaid ontop of the graphical representation 508 for easy comparison. Forexample, in the graphical representation 516, the first activation 510is virtually eliminated due to inconsistency demonstrated in the signalU1. More specifically, as shown in FIG. 5, the deflection 502 of thesignal U1 is inconsistent with and does not appear in other neighboringsignals U0 and U2. Thus, the first activation 510 is effectivelyeliminated from the filtered activation waveform A. In contrast, asshown in FIG. 5, the defections 504 and 506 of the signal U1 areconsistent with the deflections 504 and 506 of the neighboring signalsU0 and U2. Hence, in FIG. 5D, a second activation 512 corresponding tothe defection 504 and a third activation 514 corresponding to thedeflection 506 remain in the filtered activation waveform A. Due to theremoval of activations with inconsistent deflections, an accuracy of acardiac map may be greatly improved.

Embodiments of the method 400 also include determining one or moreactivation durations (block 412), which may represent a length of anactivation. That is, for example, an EGM may include a portion thereoffor which all of the amplitudes deviate beyond the signal baselineaccording to the specified criteria. The length of the time periodcorresponding to that portion of the EGM may be identified as anactivation duration. In embodiments, the activation waveform may berepresented along a time scale, in which case, the waveform mayrepresent the activation duration. For example, the width of thedeflection in the activation waveform may represent the duration of thecorresponding activation.

Embodiments of the method 400 further include aggregating the detectedactivations (block 414) such as, for example, by generating one or moreactivation waveforms, activation histograms, and/or the like. Theactivation waveforms and/or activation histograms may be used infacilitating presentation of the cardiac map (block 416). For example,embodiments may include annotating an electroanatomical map (e.g., acardiac map) based on one or more annotation waveforms, annotationhistograms, and/or the like. Additionally, or alternatively, theannotation waveforms and/or annotation histograms may be used infacilitating other processes such as, for example, ablation, recordinginformation, diagnosis, and/or the like. That is, for example, inembodiments, annotation waveforms and/or annotation histograms may beused in the creation of a cardiac map (e.g., as part of a beatacceptance step such as, e.g., the beat acceptance step 308 depicted inFIG. 3), the annotation of a cardiac map (e.g., to annotate activationtimes), the display of a cardiac map (e.g., to facilitate a display ofthe spatial and/or temporal distribution of activation times), theaugmenting of information (e.g., to facilitate determining and/orhighlighting (e.g., emphasizing computationally and/or visually)characteristics of EGMs (which may be displayed) and/or EGMs havingcertain characteristics), an ablation procedure (e.g., to detectactivations, distinguish between activations and artifacts, etc.),and/or the like. For example, in embodiments, annotation waveformsand/or annotation histograms may be used to facilitate quantification ofspecific EGM characteristics (e.g., by using activation waveforms todetermine metrics such as, e.g., a portion of time during which achannel was active (activation duration), etc.).

In embodiments, a cardiac map may be generated and/or annotated based,at least in part, on the cardiac electrical signal features and/or theactivation waveform (which may also be a cardiac electrical signalfeature). In embodiments, the cardiac map may also be generated and/orannotated, at least in part, using any number of other signals,techniques, and/or the like. For example, embodiments may utilizeimpedance mapping techniques to generate and/or annotate one or moreportions of the cardiac map such as, for example, an anatomical shellupon which electrical signal features are represented. In embodiments, asurface may be fitted on one or more of the points associated with thecardiac electrical signals to generate a shell representing theendocardial surface of the one or more cardiac structures. Inembodiments, a surface may also be fitted on one or more of the pointsassociated with the cardiac electrical signals to generate a shellrepresenting an epicardium surface or other excitable cardiac tissue. Inembodiments, one or more of the cardiac electrical signal features atthe corresponding points can be included on the shell to generate acardiac map of the one or more cardiac structures. For example,embodiments may include displaying annotations on the cardiac map thatrepresent features, extracted from the cardiac electrical signals and/orderived from other features, such as, for example, activation times,minimum voltage values, maximum voltages values, maximum negativetime-derivatives of voltages, instantaneous potentials, voltageamplitudes, dominant frequencies, peak-to-peak voltages, and/or thelike.

Cardiac electrical signal features may be represented on the cardiac mapand may be, or include, any features extracted from one or morecorresponding sensed cardiac electrical signals and/or derived from oneor more of such features. For example, a cardiac electrical signalfeature may be represented by a color, such that if the cardiacelectrical signal feature has an amplitude or other value within a firstrange then the cardiac electrical signal feature may be represented by afirst color, whereas if the cardiac electrical signal feature has anamplitude or other value that is within a second range that is differentthan the first range, the cardiac electrical may be represented by asecond color. As another example, the cardiac electrical signal featuremay be represented by a number (e.g., a 0.2 mV sensed cardiac electricalsignal feature can be represented by a 0.2 at its respective position onthe surface map). Examples of a cardiac electrical signal feature thatcan be represented at the first surface point include, but are notlimited to, an activation, an activation time, an activation duration,an activation waveform, a filtered activation waveform, an activationwaveform characteristic, a filtered activation waveform characteristic,a minimum voltage value, a maximum voltages value, a maximum negativetime-derivative of voltage, an instantaneous potential, a voltageamplitude, a dominant frequency, a peak-to-peak voltage, and/or thelike.

In embodiments, other features such as, for example, non-electricalsignal features, non-cardiac electrical signal features, and/or thelike, can be represented on an anatomical map at respective locations.Examples of non-electrical signal features include, but are not limitedto, features derived from magnetic resonance imaging, a computerizedtomography scan, ultrasonic imaging, and/or the like.

According to embodiments, activation waveforms, as described above, maybe useful for facilitating any number of different functionalities. Forexample, in embodiments, activation waveforms may be used to generateactivation maps that more clearly represent activation propagation. Inembodiments, activation waveforms may facilitate automaticclassification of electrical signals such as EGMs. Activation waveformsmay be used to facilitate cardiac mapping tools such as, for example,tools that facilitate accurate interpretation of activation maps. Forexample, embodiments facilitate generating activation histogramsrepresenting classifications associated with amounts of tissueactivating at each activation time within a specified time period.Activation histogram waveforms may be presented on a display device, andmay be associated with a cardiac map. Activation histogram waveforms mayfacilitate identifying and focusing on certain cardiac events, smalltissue regions with activation times satisfying a certain set ofcriteria, and/or the like. Similarly, local activation histograms mayfacilitate map interpretation and navigation by representing aggregatedactivity across smaller regions of tissue.

Various modifications and additions can be made to the exemplaryembodiments discussed without departing from the scope of the presentdisclosure. For example, while the embodiments described above refer toparticular features, the scope of this disclosure also includesembodiments having different combinations of features and embodimentsthat do not include all of the described features. Accordingly, thescope of the present disclosure is intended to embrace all suchalternatives, modifications, and variations as fall within the scope ofthe claims, together with all equivalents thereof.

We claim:
 1. A system for processing cardiac information, the systemcomprising: a processing unit configured to: receive a set of cardiacelectrical signals; identify, for each electrical signal of the set ofelectrical signals, a deflection, the deflection comprising a deviationfrom a signal baseline; generate, based on the identified deflections,an activation waveform corresponding to the set of electrical signals,wherein the activation waveform comprises values corresponding toprobabilities of whether the identified deflections represent tissueactivations; and facilitate presentation, on a display device, arepresentation of the activation waveform.
 2. The system of claim 1,wherein the processing unit is further configured to: receive anindication of a measurement location corresponding to each of the set ofelectrical signals; and generate one or more cardiac map annotationsbased on the activation waveform.
 3. The system of claim 1, wherein theprocessing unit is further configured to determine the signal baseline.4. The system of claim 3, wherein the processing unit is configured todetermine the signal baseline by referencing a memory in which thesignal baseline is stored, the signal baseline comprising apre-determined value.
 5. The system of claim 3, wherein the processingunit is configured to determine the signal baseline based on anestimated noise floor associated with at least one electrical signal ofthe set of electrical signals.
 6. The system of claim 1, wherein theprocessing unit is configured to identify, for each electrical signal ofthe set of electrical signals, a deflection by identifying, for eachsample point of each electrical signal, whether the sample pointrepresents a deviation from the signal baseline.
 7. The system of claim1, wherein the set of electrical signals comprises a plurality ofelectrical signals, wherein the processing unit is configured to adjustan activation waveform value corresponding to a sample point based on anevaluation of spatiotemporal consistency of corresponding deflections ofthe plurality of electrical signals.
 8. The system of claim 1, whereinthe set of electrical signals are represented as ordered values and thesignal baseline is a percentile of the ordered values.
 9. The system ofclaim 8, wherein the percentile is the 20^(th) percentile.
 10. Thesystem of claim 1, wherein the signal baseline is a range of values. 11.A method of processing cardiac information, comprising: receiving a setof cardiac electrical signals; identifying, for each electrical signalof the set of electrical signals, a deflection, the deflectioncomprising a deviation from a signal baseline; generating, based on theidentified deflections, an activation waveform corresponding to the setof electrical signals, wherein the activation waveform comprises valuescorresponding to probabilities of whether the identified deflectionsrepresent tissue activations; and facilitating presentation, on adisplay device, of a representation of the activation waveform.
 12. Themethod of claim 11, further comprising: receiving an indication of ameasurement location corresponding to each of the set of electricalsignals; and generating one or more cardiac map annotations based on theactivation waveform.
 13. The method of claim 11, further comprisingdetermining the signal baseline.
 14. The method of claim 13, wherein thesignal baseline is determined based on an estimated noise floorassociated with at least one electrical signal of the set of electricalsignals.
 15. The method of claim 11, wherein the set of electricalsignals comprises a plurality of electrical signals, the method furthercomprising adjusting an activation waveform value corresponding to asample point based on an evaluation of spatiotemporal consistency ofcorresponding deflections of the plurality of electrical signals. 16.The method of claim 15, wherein the set of electrical signals arerepresented as ordered values and the signal baseline is a percentile ofthe ordered values.
 17. One or more computer-readable media havingembodied thereon computer-executable instructions that, when executed bya processor, are configured to cause the processor to instantiate one ormore program components, the one or more program components comprising:an acceptor configured to: receive a set of cardiac electrical signals;and an annotation waveform generator configured to: identify, for eachelectrical signal of the set of electrical signals, a deflection, thedeflection comprising a deviation from a signal baseline; and generate,based on the identified deflections, an activation waveformcorresponding to the set of electrical signals, wherein the activationwaveform comprises values corresponding to probabilities of whether theidentified deflections represent tissue activations.